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An introduction to deep learning on biological sequence data: examples and solutions

机译:生物序列数据深度学习介绍:例子和解决方案

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摘要

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology.
机译:诸如卷积和长短期记忆网络等深度神经网络架构在近年来越来越受到机器学习工具的越来越受欢迎。 更大的计算资源可用性,更多的数据,用于训练深层模型的新算法以及易于使用的内部网络的培训和培训是神经网络的培训是这一发展的驱动因素。 深度学习的使用在图像识别中特别成功; 在此字段中的大多数情况下,在大多数情况下,在大多数情况下都是在此字段中而不是生物学中的开发。

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